{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This allows multiple outputs from a single jupyter notebook cell:\n",
    "from IPython.core.interactiveshell import InteractiveShell\n",
    "InteractiveShell.ast_node_interactivity = \"all\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "<a name=\"mcoverride\"></a>\n",
    "# Coloring Individual Candlesticks\n",
    "\n",
    "- Users can **color one or more specific candlesticks (or ohlc bars) differently than others**.\n",
    "  - Users may choose to do this in order to highlight a specific pattern or technical signal.\n",
    "\n",
    "\n",
    "- The **`marketcolor_overrides` kwarg** is used to specify individual candle colors.\n",
    "\n",
    "\n",
    "- **`marketcolor_overrides`** must be set it to an interable (`list`,`tuple`,`ndarray`) **that is the *same length as the dataframe*** being plotted.\n",
    "  - The simplest way to do this is to create a \"marketcolor overrides\" column in the dataframe.\n",
    "\n",
    "\n",
    "- **Rows where the user wants to override the candle color must contain a \"color-like\" object:**\n",
    "  - Examples of color-like objects include:\n",
    "    - a **string** such as `'yellow'` or `'#ffff00'`\n",
    "    - an **rgb or rgba tuple** such as `(1.0, 1.0, 0)` or `(1.0, 1.0, 0, 0.75)`, or `(255, 255, 0)` or `(255, 255, 0, 0.75)`\n",
    "    - an **mplfinance marketcolor object**, created with `mpf.make_marketcolors()`\n",
    "\n",
    "\n",
    "- **Rows where the user does *NOT* want to override** the candle color ***MUST*** contain **`None`** values.\n",
    "\n",
    "---\n",
    "---\n",
    "\n",
    "### To illustrate, we will first give simple examples using just a few candles.\n",
    "### This is followed by examples using more realistic, larger, data sets:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pandas version= 1.1.2\n",
      "mplfinance version= 0.12.8b4\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "print('pandas version=',pd.__version__)\n",
    "\n",
    "import mplfinance as mpf\n",
    "print('mplfinance version=',mpf.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Start with a simple, 5-row DataFrame:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2021-10-11</th>\n",
       "      <td>131.4</td>\n",
       "      <td>133.2</td>\n",
       "      <td>131.3</td>\n",
       "      <td>132.1</td>\n",
       "      <td>19591</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-12</th>\n",
       "      <td>131.9</td>\n",
       "      <td>132.7</td>\n",
       "      <td>131.3</td>\n",
       "      <td>131.4</td>\n",
       "      <td>21467</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-13</th>\n",
       "      <td>132.0</td>\n",
       "      <td>133.2</td>\n",
       "      <td>131.5</td>\n",
       "      <td>131.8</td>\n",
       "      <td>20406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-14</th>\n",
       "      <td>130.9</td>\n",
       "      <td>132.7</td>\n",
       "      <td>130.6</td>\n",
       "      <td>132.1</td>\n",
       "      <td>22611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2021-10-15</th>\n",
       "      <td>131.6</td>\n",
       "      <td>131.8</td>\n",
       "      <td>130.7</td>\n",
       "      <td>131.0</td>\n",
       "      <td>22001</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Open   High    Low  Close  Volume\n",
       "2021-10-11  131.4  133.2  131.3  132.1   19591\n",
       "2021-10-12  131.9  132.7  131.3  131.4   21467\n",
       "2021-10-13  132.0  133.2  131.5  131.8   20406\n",
       "2021-10-14  130.9  132.7  130.6  132.1   22611\n",
       "2021-10-15  131.6  131.8  130.7  131.0   22001"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x575 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ix = pd.DatetimeIndex(['2021-10-11','2021-10-12','2021-10-13','2021-10-14','2021-10-15'])\n",
    "\n",
    "df = pd.DataFrame(dict(  Open=[131.4, 131.9, 132.0, 130.9, 131.6],\n",
    "                         High=[133.2, 132.7, 133.2, 132.7, 131.8],\n",
    "                          Low=[131.3, 131.3, 131.5, 130.6, 130.7],\n",
    "                        Close=[132.1, 131.4, 131.8, 132.1, 131.0],\n",
    "                       Volume=[19591, 21467, 20406, 22611, 22001]),\n",
    "                  index=ix)\n",
    "df\n",
    "mpf.plot(df,volume=True,style='yahoo',type='candle')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--- \n",
    "\n",
    "#### Suppose we want to color the third candle yellow, and the fourth candle blue.\n",
    "#### Then we create a list of overrides as follows: `mco = [None,None,'yellow','blue',None]`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x575 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mco = [None,None,'yellow','blue',None]\n",
    "mpf.plot(df,volume=True,style='yahoo',type='candle',marketcolor_overrides=mco)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "#### We can change only the \"face\" (body) of the candle, with the `mco_faceonly` kwarg:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x575 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mco = [None,None,'yellow','blue',None]\n",
    "mpf.plot(df,volume=True,style='yahoo',type='candle',marketcolor_overrides=mco,mco_faceonly=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "#### marketcolor overrides work also with ohlc plots\n",
    "- Presently only `type='candle'` and `type='ohlc'` are supported.\n",
    "- If there is enough demand, we will consider also supporting types 'hollow_and_filled', 'renko', and 'point_and_figure'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x575 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mco = [None,None,'yellow','blue',None]\n",
    "mpf.plot(df,volume=True,style='yahoo',type='ohlc',marketcolor_overrides=mco)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "#### We can also fully customize indivual candles using a marketcolors object.\n",
    "#### Notice that with the approach, we do not have to know whether a candle is up or down;<br> a single marketcolors object can specify different colors for up candles and down candles:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x575 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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AAADTEToBAABgOkInAACAH2zatEnDhw9XfHy8QkNDtWbNmjLzZ8yYoaSkJLVp00bR0dFKTk7Wli1bfPP37duncePGqVOnTmrdurU6d+6s5557Tm63u9J+T5w4ofHjxysmJkYXXXSRRo0apZycHFPW8VwQOgEAAPygsLBQCQkJeuGFFyqcf/HFF+v555/Xpk2b9NFHHykyMlKDBw/W4cOHJUn/+c9/5PV69eKLL+rrr7/Ws88+q9dff13Tpk2rtN8pU6Zo7dq1euONN7R69WodPHhQt956a43Wwe12Kz09XcXFxTX6/spw9zoAAIAf9OvXT/369Tvj/GHDhpV5/cwzzyglJUXbt29Xr169dM011+iaa67xzY+OjlZGRoZee+21MwbPvLw8vfXWW1q0aJGuuuoqSdIrr7yirl27avPmzUpMTKzS2AsLCzVp0iQtXbpUkrRlyxZFR0dr4sSJat26tR588MEqLacy1QqdHo9HDoej3LRTv9Y3Hm/JM4SMn/9ZxjB8Xw2HdY8zKV1Hj9db737m3p9/1t56uO6lMjIy9NNPFj+659BOBUnauXOnHIct7VrNmjVT+/btre30FB5Pye+28fN/ljJ++Wo4rOu7dD09Hq88HovX2Wb1/f3ULpXV3fj5vTY/P79M/gkKClJQUNA59et2u/Xmm28qODhYCQkJZ2yXn5+v0NDQM87/7rvvVFRUpN69e/umXXLJJbrooouqFTqffvppbdu2TatWrSoTjnv37q0ZM2ZYHzrT09N9P4DTZWRknPNgaqPMkyXXTHg8xXJ6rd9BWr1z8hgl/WXuzVTjoOOW9m230utj9u/fr5MnT9o8GutlZmZq0KBBlvfbopE0pIP0Xsr/KNeGTe5vf/uboqKirO9YUmZmI0mXyOvxylNsTxCxeh/j9ZT8cZeZmanGjevXPqZUfX0/tVtFdXc4HIqKilJCQoIKTvnEgkmTJmny5Mk16mft2rUaO3asCgsLFRERoZUrV6pFixYVtt29e7deffXVSk+vZ2dnq0GDBgoJCSkzvVWrVsrOzq7yuNasWaPXXntNiYmJZQL2pZdeqr1791Z5OZWpVuiMjY2t8EhnRkaG2rdvL5fLuoejny8K8xtJWZLLFSCXhQ+Hl2HI4/GU1NzCI51er0PySlHRUYoLjrSs3/NB6V+1bdu2VXR0tL2DsUFhYaEkKSUkRfEB8Zb1a8iQ979e3dHYKUdj67b1ncU7dWverWrZsqXi4uIs6/dUhYUl6+t0OeUKsHj/auiUfYx13TpdJbcaREVFKS6u/h3prM/vp3aprO6GYcjtdmvbtm3ljnTWVM+ePbVx40bl5uZqyZIlGjNmjNatW6eWLVuWaXfgwAENHTpUycnJGj16dI37q6rc3NxyY5CkY8f892lo1QqdLperXOg8dV59/CVxOUt2kA5Zul/+5ZS6w9rPCinty+V01ruft/Pnn7WzHq67JN86xwfG61eBv7KuY0MqdhYrwBVg7S/Zz33ZuW8r7dbx839W8p1Sd8jSvkv7crmcqoe/ZpLq7/up3Sqqe+nZ3eDg4DPmn+pq0qSJYmJiFBMTo8TERHXp0kUpKSl66KGHfG2ysrI0cOBAJSUl6aWXXqp0eeHh4XK73crLyytztDMnJ0fh4eFVHtcVV1yhTz75RHfeeack+dY3JSVFSUlJ1VjDM+NGIgAAAJt4vd4yj0Q6cOCABg4cqE6dOmnevHm+Ax5n0qlTJwUGBmrDhg0aOHCgpJLLIX/44YcqX88pSY899phuvPFGff/99youLtaf//xn7dq1S99++61Wr15ds5U7DY9MAgAA8IOCggKlpaUpLS1NUsm1yWlpadq/f7+OHTump59+Wps3b9a+ffuUmpqqe++9V1lZWb7r5Q8cOKABAwbooosu0rRp03T48GFlZ2eXuTbzwIEDSkpK0tatWyVJISEhGjlypKZOnaq///3vSk1N1R//+EclJiZWK3R269ZNGzdulMfjUYcOHbR+/XpdcMEF+vjjj3XFFVf4pT4c6QQAAPCD1NRUDRgwwPd66tSpkqQRI0Zozpw5Sk9P17Jly5Sbm6uwsDB17txZH374oeLjS66T/+KLL7R7927t3r1bHTt2LLPso0ePSpKKi4uVnp6u48d/udFu+vTpcjqdGjVqlNxut/r06aNZs2ZVe/zt2rXT3Llzq/19VUXoBAAA8IMePXr4wmFFUlJSKv3+m2++WTfffHOlbSIjI8v10bBhQ82aNatGQbPUJ598IpfLpb59+5aZ/tlnn8nr9Vb6/NGq4vQ6AABAPffUU0+d8VmlTz31lF/6IHQCAADUc7t379all15abvoll1yiPXv2+KUPTq+jdtq3T/r5uZFWcRw8WPI1M1Oy4+HwjRtLkfXr2agAAGsEBwdr7969ijztfWb37t1q3LixX/ogdKL22bdPAUNvtLzbCwICNPCCFrpgyqMKKC62vH9JKl7+DsETAOB31113nR555BG99dZbateunaSSwPnoo4/quuuu80sfhE7UPj8f4TSaNpUCrNuEQ2TohoJjCmjaRIbFD+pWcbEcBQWWH90FANQPTz31lIYNG6akpCRdeOGFkkoez9StW7dKP4azOgidqL0CAiwNnZLxS79Wh04AAEwUEhKijz/+WOvXr9e2bdvUsGFDdezYUVdeeaXf+iB0AgAAQA6HQ3369FGfPn1MWT6hEwAAoB5auHChRo8erYYNG2rhwoWVtr3rrrvOuT9CJwAAQD00f/58DRs2TA0bNtT8+fPP2M7hcBA6AQAAUDPfffddhf9vFh4ODwAAUI8VFRWpc+fO2rVrl6n9EDoBAADqscDAQJ204ENPOL0OoFp2Fu+0tkND8ng8cnldlj6pyvL1BAAb/c///I/mzp2rl19+WQEmPY6Q0AmgSpo2bSpJuvXHWy3tt0UjaUgH6b0dUu5xS7uW9Mt6A2bLy8vT119/rYiICIWFhdk9HNQz//rXv7Rx40atX79eHTp0KPfRlykpKefcB6ETQJXExsZq+/btKigosLRfb84OBa4aozFPvi5nqw6W9t20aVPFxsZa2ifqr7y8PH3zzTfq06cPoROWCwkJ0YABA0ztg9AJoMrsCGAFez3avUqKi4tT0+jOlvcPAHWZ1+vVyy+/rIyMDBUVFalnz56aPHmyGjVq5Pe+uJEIAACgnpo9e7amTZumpk2bqnXr1nr11Vc1YcIEU/oidAIAANRTy5Yt06xZs/Tee+/pL3/5i5YuXap3331XXq/X730ROgEAAOqpH374Qf369fO97t27txwOh7KysvzeF6ETAACgniouLlbDhg3LTAsMDFRxcbHf++JGIgAAgHrKMAzdc889CgoK8k07ceKEHnrooTKPTeKRSQAAAKixESNGlJt24403mtIXoRMAAKCemjdvnmV9cU0nAAAATEfoBAAAgOkInQAAADAdoRMAAACmI3QCAADAdIROAAAAmI7QCQAAANMROgEAAGA6QicAAABMxycSAQBwun37pMJCS7t0HDxY8jUzUzp50tK+1bixFBlpbZ+odwidAACcat8+BQw157OnK3NBQIAGXtBCF0x5VAHFxZb3X7z8HYInTEXoBADgVD8f4TSaNpUCrHubDJGhGwqOKaBpExlyWNaviovlKCiw/Mgu6h9CJwAAFQkIsDR0SsYv/VoZOgGLcCMRAAAATEfoBAAAgOkInQAAADAdoRMAAMAPNm3apOHDhys+Pl6hoaFas2ZNmfkzZsxQUlKS2rRpo+joaCUnJ2vLli1l2syaNUvXXnutLrzwQkVFRVWp33vuuUehoaFl/g0dOtRv6+UvhE4AAAA/KCwsVEJCgl544YUK51988cV6/vnntWnTJn300UeKjIzU4MGDdfjwYV+boqIiJScn6/bbb69W33379tX333/v+7d48eJzWhczcPc6AACAH/Tr10/9+vU74/xhw4aVef3MM88oJSVF27dvV69evSRJjzzyiCTp7bffrlbfQUFBCg8Pr+aIrVWt0OnxeORwOMpNO/VrfePxeiWVPOjCsLJjw/B9NRzWPVqjdB09Xq9tP3PHzzW3uuqnlFwOh6U/bZWup9frlVHPfte8P/+8vTZuc3bxeEp+t42f/7OU8ctXw8LtvXQ9PR6vPB6rf89K1L99TP3dv0iV5xjj5x9Kfn5+mfwTFBSkoKCgc+rX7XbrzTffVHBwsBISEs5pWZL05ZdfKjY2Vs2bN1fPnj316KOPKiws7JyX60/VCp3p6em+H8DpMjIy/DKg2ibzZI4kyeMpltNr/Q7S6jdhj1HSX+beTDUOOm5p36Ua7c1UrKTiYnt2jh6P9Z8UomKPAlVSd3uqbh/j2HE5fn2X9ucel+PELruHY6nMzEaSLpHX45XHtu3d2n69npLAl5mZqcaN2cdYoh7vX05VUY5xOByKiopSQkKCCgoKfNMnTZqkyZMn16iftWvXauzYsSosLFRERIRWrlypFi1a1HjcUsmp9f79+ysqKkp79+7VtGnTNGzYMH3yySdyuVzntGx/qlbojI2NrfBIZ0ZGhtq3b39erZhVCvMbSVmSyxUgl9PC9TcMeTyekppbeKTT63VIXikqOkpxwfZ8XFrp2gYEuCx9cLNhlLwZuFwBVpa8jKjoKBlxcfZ0bhOPp70ymrSsl/uYwsKSDc3pcsoVYPG6GzplH2Ndt05Xya0GUVFRiouz6Ujnz1/r2z6mPu5fpMpzjGEYcrvd2rZtW7kjnTXVs2dPbdy4Ubm5uVqyZInGjBmjdevWqWXLljVe5pAhQ3z/37FjR3Xs2FGdO3fWl19+6Tttfz6o1m+Ty+UqFzpPnVff3hAkyeUs2UE6ZO3nR/hOqTsclvZb2pfL6bTv5+0svf/N2qqXnu4qKb3V7wg/hw+nU6qHv2dS/dzHlK6u4+f/rOQ7pe6QpX2X9uVyOe3b1OvdPob9i1TxPqb07G5wcPAZ8091NWnSRDExMYqJiVFiYqK6dOmilJQUPfTQQ35ZviRFR0erRYsW2r1793kVOrl7HQAAwCZer1dut9uvy/y///s/HTly5Ly7sYjQCQAA4AcFBQVKS0tTWlqapJJrk9PS0rR//34dO3ZMTz/9tDZv3qx9+/YpNTVV9957r7KysjRo0CDfMvbv36+0tDT98MMP8nq9vuWdek1pUlKSVq9e7evzscce8y13w4YNuuWWWxQTE6O+fftaW4Cz4JFJAAAAfpCamqoBAwb4Xk+dOlWSNGLECM2ZM0fp6elatmyZcnNzFRYWps6dO+vDDz9UfHy873uee+45LV261Pf6qquukiStWrVKPXr0kFRyY3d+fr6kkssCduzYoWXLlikvL08RERHq06ePpkyZcs532PsboRMAAMAPevTooaNHj55xfkpKylmXMX/+fM2fP7/SNqf20ahRI7333ntVH6SNOL0OAAAA0xE6AQAAYDpOr/tJseGRvGdv5y+GSh7U7vVa+yCVYuM8+rSKYqsf0m5IvodFW/zIJMvXFQAA/yJ0nqOmAQ0lST8WW/s5Do1OSHF7pF3tpOMNLe1a0i/rbYvGjSVJjlPu5LPCjwEB+uKCFup9OFfN7QqBP687AAC1DaHzHMU2Cdf2XtNUUHzC0n6z9x/QirWv6+nfjlF42wst7btpQEPFNrHx2V+RkSpe/o5UWGhpt4cPHtQHb7+thIceVNOICEv7llQSOCPt+RQoAADOFaHTD+wIYHuOlJzLj2sSoXYhUZb3bzsbwpfx86MnjKgoqV07y/sHAKA240YiAAAAmI7QCQAAANMROgEAAGA6QicAAABMR+gEAACA6QidAAAAMB2hEwAAAKYjdNZSISEh6tq1q0JCQuweSr1BzQEAqDlCZy0VEhKibt26EYAsRM0BAKg5QicAAABMR+gEAACA6QidAAAAMB2hEwAAAKYjdAIAAMB0hE4AAACYjtAJAAAA0xE6AQAAYDpCJwAAAExH6AQAAIDpCJ0AAAAwHaETAAAApiN0AgAAwHSETgAAAJiO0AkAAADTEToBAABgOkInAAAATEfoBAAAgOkInQAAADAdoRMAAACmI3QCAADAdIROAAAAmI7QCQAAANMROgEAAGA6QicAAABMR+gEAADwg02bNmn48OGKj49XaGio1qxZU2b+jBkzlJSUpDZt2ig6OlrJycnasmVLmTazZs3StddeqwsvvFBRUVFV6tcwDE2fPl2XXnqpWrdureTkZP33v//123r5C6ETAADADwoLC5WQkKAXXnihwvkXX3yxnn/+eW3atEkfffSRIiMjNXjwYB0+fNjXpqioSMnJybr99tur3O/cuXO1cOFCzZkzR59++qkaN26sIUOG6MSJE+e8Tv4UUJ3GHo9HDoej3LRTv8Ia1N161Nwe9bnuHk/J/tb4+T9LGb98NRzW9V26nh6PVx6Pxev8M4fXW/I/xcWShXU3DEkejwxDOu2t1lzFJb9bXq9XRr38PTvzPsYwSn7++fn5ZfJPUFCQgoKCyrXv16+f+vXrd8a+hg0bVub1M888o5SUFG3fvl29evWSJD3yyCOSpLfffrtK4zcMQ3/+8581fvx4XX/99ZKkBQsWKC4uTmvWrNGQIUOqtBwrVCt0pqen+34Ap8vIyPDLgFA91N161Nwe9bHumZmNJF0ir8crT7E9YcDqsO/1lAS+zMxMNW583NK+SzU4mK1LJTkKCiztNy8gQF9c0EK9D+eqeXGxpX1L0u6D2XJb3uv5o6J9jMPhUFRUlBISElRwyvYwadIkTZ48+Zz6c7vdevPNNxUcHKyEhIQaLyczM1PZ2dnq3bu3b1pISIi6dOmizZs3197QGRsbW+GRzoyMDLVv314ul8uvg8OZUXfrUXN71Oe6FxaW7G+dLqdcARavu1FSe5fLJVl41M3pKrnqKyoqSnFx9hzpVFyc3O8skwoLLe02OytLH/z1r4q//z41bt3a0r7VuLHatW1rbZ/nicr2MYZhyO12a9u2beWOdNbU2rVrNXbsWBUWFioiIkIrV65UixYtary87OxsSVLLli3LTG/VqpVycnJqvFwzVCt0ulyucqHz1Hn17Q3hfEDdrUfN7VEf6166uo6f/7OS75S6Q5b2XdqXy+WUrT/u6GjLu3Q2alTyNSZGznbtLO+/vqtoH1N6djc4OPiM+ae6evbsqY0bNyo3N1dLlizRmDFjtG7dunKhsS7iRiIAAACLNGnSRDExMUpMTNSf/vQnBQQEKCUlpcbLCw8PlyQdOnSozPScnBy1atXqnMbqb4ROAAAAm3i9XrndNb+aNioqSuHh4dqwYYNvWn5+vrZu3arExER/DNFvqnV6HQAAABUrKCjQnj17fK8zMzOVlpam5s2bKywsTLNnz9Z1112n8PBwHTlyRIsXL1ZWVpYGDRrk+579+/frxx9/1A8//CCv16u0tDRJUrt27dS0aVNJUlJSkh5//HH1799fDodDd999t2bNmqWYmBhFRUVp+vTpioiI0A033GBtAc6C0AkAAOAHqampGjBggO/11KlTJUkjRozQnDlzlJ6ermXLlik3N1dhYWHq3LmzPvzwQ8XHx/u+57nnntPSpUt9r6+66ipJ0qpVq9SjRw9JJU8Tys/P97W5//77VVhYqAcffFB5eXn6zW9+o+XLl6thw4amrm91EToBAAD8oEePHjp69OgZ51fl2s358+dr/vz5lbY5vQ+Hw6EpU6ZoypQpVRuoTbimEwAAAKYjdAIAAMB0nF4HgPPc/p1NLO/TkCGvxyuny2npczrtWFcA1iB0AsB56ucbVTXz1sst77tFoywN6bBQ7+24S7nHLf50HP2y7gDqDkInAJynYmOl7duLZPFHgEuSvDmZClz1lMY8+Ts5W11gad9Nm5asO4C6hdAJAOcxu8JXwV5Du1dJcXGGmkbbMwYAdQs3EgEAAMB0hE4AAACYjtAJAAAA0xE6AQAAYDpCJwAAAExH6AQAAIDpCJ0AAAAwHaETAAAApiN0AgAAwHSETgAAAJiO0AkAAADTEToBAABgOkInAAAATEfoBAAAgOkInQAAADAdoRMAAACmI3QCAADAdIROAADOAyEhIeratatCQkLsHgpgCkInAADngZCQEHXr1o3QiTqL0AkAAADTEToBAABgOkInAAAATEfoBAAAgOkInQAAADAdoRMAAACmI3QCAADAdIROAAAAmI7QCQAoJyA4Qo5f36WA4Ai7hwKgjiB0AgDKCQxpLVfSHxQY0truoQCoIwidAAAAMB2hEwAAAKYjdAIAAMB0hE4AAACYjtAJAADgB5s2bdLw4cMVHx+v0NBQrVmzpsz8GTNmKCkpSW3atFF0dLSSk5O1ZcuWMm2OHj2qO+64Q5GRkYqKitK4ceNUUFBQab/9+/dXaGhomX8PPvig39fvXBE6AQAA/KCwsFAJCQl64YUXKpx/8cUX6/nnn9emTZv00UcfKTIyUoMHD9bhw4d9be644w59//33WrFihZYtW6avvvpKDzzwwFn7Hj16tL7//nvfv6eeespfq+U3AXYPAAAAoC7o16+f+vXrd8b5w4YNK/P6mWeeUUpKirZv365evXpp165d+uyzz/T555+rc+fOkqSZM2fqxhtv1LRp09S69ZkfYdaoUSOFh4f7Z0VMUq3Q6fF45HA4yk079SusQd2tR83tQd3tQd2tR83tUVndDcOQJOXn55fJP0FBQQoKCjqnft1ut958800FBwcrISFBkrR582aFhIT4Aqck9e7dW06nU1u3blX//v3PuLx3331X77zzjlq1aqXf/e53mjBhgho3bnxOY/S3aoXO9PR03w/gdBkZGX4ZEKqHuluPmtuDutuDuluPmtujoro7HA5FRUUpISGhzHWVkyZN0uTJk2vUz9q1azV27FgVFhYqIiJCK1euVIsWLSRJ2dnZatmyZZn2AQEBCg0NVXZ29hmXOXToULVt21YRERHavn27nnrqKWVkZCglJaVGYzRLtUJnbGxshUc6MzIy1L59e7lcLr8ODmdG3a1Hze1B3e1B3a1Hze1RWd0Nw5Db7da2bdvKHemsqZ49e2rjxo3Kzc3VkiVLNGbMGK1bt65c2KyO2267zff/HTt2VEREhAYNGqQ9e/aoXbt2NV6uv1UpdJ56yPn00OlwOOR0OuVwOMrNg3mou/WouT2ouz2ou/WouT0qq3vp2d0mTZooIMA/t8E0adJEMTExiomJUWJiorp06aKUlBQ99NBDCg8P16FDh8q0Ly4u1tGjR6t1vWaXLl0kSbt37659obOU2+2ucHpkZKSKiopUVFTkl0Ghaqi79ai5Pai7Pai79ai5Peysu9fr9eWrxMRE5eXlKTU1VVdccYUkaePGjfJ6vb4gWRVpaWmSdN7dWFSt0NmwYcMKT6+np6crNjaW0wEWou7Wo+b2oO72oO7Wo+b2qKzuhmHoxIkTVV5WQUGB9uzZ43udmZmptLQ0NW/eXGFhYZo9e7auu+46hYeH68iRI1q8eLGysrI0aNAgSVJcXJz69u2r+++/X3PmzFFRUZEmTpyowYMH++5cP3DggJKTk7VgwQJ16dJFe/bs0fLly9WvXz+FhYVp27Ztmjp1qrp37+67Qel8UaXQWfpDqOjQs8PhkGEYnA6wGHW3HjW3B3W3B3W3HjW3R1XqXtU/AlJTUzVgwADf66lTp0qSRowYoTlz5ig9PV3Lli1Tbm6uwsLC1LlzZ3344YeKj4/3fc+iRYs0YcIEJScny+FwaODAgZoxY4ZvfnFxsdLT03X8+HFJUmBgoL744gstWLBAhYWFatOmjQYMGKDx48dXuxZm4zmdAAAAftCjRw8dPXr0jPOrcjd5aGioFi9efMb5kZGRZfq46KKLyn3y0fmKTyQCAACA6QidAAAAMB2hEwAAAKYjdAIAAMB0hE4AAACYjtAJAAAA0xE6AQAAYDpCJwAAAExH6AQAAIDp+EQiADDZrVteVa77mN3DqCZDXq8h5zdrJdWej2Rs0aCJUn59p93DAFABQicAmCzXfUyNgoLsHka9kHuytoV7oP7g9DoAAABMR+gEAACA6Ti9DgCoc5x33yMdOWL3MKrFKamD4ZXTUQuPB4WFyfvn+XaPAuc5QicAoO45ckSONhfaPYpqc9k9gBoy/u+A3UNALVAL/5wCAABAbcORTtimtp3+4tQXAAA1R+iEfWrh6S9OfQEAUDO18JANAAAAahtCJwAAAExH6AQAAIDpCJ0AAAB+MGfOHPXp00dt27ZVbGysbrnlFqWnp5dpc+LECY0fP14xMTG66KKLNGrUKOXk5JRps3//ft1444268MILFRsbq8cee0zFxcVl2nz55Zfq1auXwsPD9atf/Upvv/12ufEsWrRIl19+uSIiInTNNddo69at/l/paiB0AgAA+MFXX32lsWPH6pNPPtGKFStUVFSkwYMH69ixY742U6ZM0dq1a/XGG29o9erVOnjwoG699VbffI/Ho5tuuklFRUX6+OOPNX/+fC1dulTTp0/3tcnMzNRNN92knj17auPGjbr77rt133336bPPPvO1WbFihR599FFNmjRJX3zxhRISEjRkyBAdOnTImmJUgLvXgXrk1i2vKtd97OwNzyuGvF5Dzm/WSnLYPZhqadGgiVJ+fafdwwBgkeXLl5d5PX/+fMXGxio1NVVXXnml8vLy9NZbb2nRokW66qqrJEmvvPKKunbtqs2bNysxMVGff/65du3apffff1+tWrXSZZddpilTpujJJ5/U5MmT1aBBA7322muKjIzUM888I0mKi4vTP/7xDy1YsEB9+/b19T1q1CjdcsstkkqOwn7yySd666239OCDD1pYlV9UK3R6PB45HI5y0079CmvUhbpzmN1aHo9Hue4CNQpqaPdQ6o3ckwU//44adg+lHjHk8XjYv9igtr4fVfZ+ahglv7v5+fll8k9QUJCCgoLOuuz8/HxJUmhoqCTpu+++U1FRkXr37u1rc8kll+iiiy7yhc7NmzerQ4cOatWqla9N37599fDDD+v777/X5Zdfrs2bN5dZRmmbRx55RJLkdruVmppaJlw6nU716tVLmzdvPuu4zVKt0Jmenu77AZwuIyPDLwNC9dTmuncwvLX2uZe1jdfwateuXfJ6CT9W8noN6m6x0pqzf7FW6T6mNqvo/dThcCgqKkoJCQkqKCjwTZ80aZImT55c6fK8Xq8eeeQRde3aVR06dJAkZWdnq0GDBgoJCSnTtlWrVsrOzpYk5eTklAmcktSyZUvf95e2KZ12apuffvpJx48f148//iiPx1Nhm9OvMbVStUJnbGxshUc6MzIy1L59e7lc/IpbpS7UvVZ+sk8t5XQ4FRcX9/MpaljF6XRQd4v5as7+xVKl+5jaqLL3U8Mw5Ha7tW3btnJHOs9m/Pjx2rlzpz766CO/j7m2qlbodLlc5ULnqfNqa/ipzag7qqpkO6ld10TWfg7qbjkH+0Sb1Pa6V/R+Wnp2Nzg4+Iz5pyITJkzQxx9/rA8//FBt2rTxTQ8PD5fb7VZeXl6Zo505OTkKDw+XVHLU8/S7zEtv/jm1zek3BB06dEjNmjVTo0aNfOtSUZvTj6JaiRuJxM0VVuPmCgBAXWQYhiZOnKg1a9Zo1apVioqKKjO/U6dOCgwM1IYNGzRw4EBJJZcu/vDDD0pMTJQkJSYmavbs2Tp06JDv9Pj69evVrFkz39HkxMREffrpp2WWvX79eiUlJUmSGjRooCuuuEIbNmzQDTfcIKnkdP/GjRs1duxY8wpwFoROSbnuY2pUhUPl8I/ck7Ut4AMAcHbjx4/X8uXL9fbbb6tp06a+azCDg4PVqFEjhYSEaOTIkZo6dapCQ0PVrFkzTZw4UYmJib7Q2adPH8XFxenuu+/Wk08+qZycHD377LMaO3as77T+7bffrsWLF+vxxx/XyJEjtXHjRr3//vv661//6hvLPffco3vuuUedO3fWr371Ky1YsEDHjh3z3c1uB0InAACAH7z22muSpP79+5eZPm/ePN18882SpOnTp8vpdGrUqFFyu93q06ePZs2a5Wvrcrm0bNkyPfzww/rtb3+rxo0ba8SIEZoyZYqvTVRUlP76179qypQpWrhwoS688EK9/PLLvsclSdLgwYN1+PBhTZ8+XTk5Obrsssu0fPlyTq8DAADUdkePHj1rm4YNG2rWrFllgubpIiMj9e6771a6nB49emjjxo2Vtrnzzjt15501u5ztq6++0htvvKG9e/fqjTfe0IUXXqhly5YpKipK3bp1q9Eyub0PAAAAPh988IGGDh2qRo0a6d///rfcbrekkueOzpkzp8bLJXQCAADAZ9asWZozZ47mzp2rwMBA3/Tf/OY3+ve//13j5RI6AQAA4JORkaHu3buXmx4cHKy8vLwaL5fQCQAAAJ9WrVpp9+7d5ab/4x//UHR0dI2XS+gEAACAz6hRo/TII49oy5YtcjgcysrK0jvvvKPHHntMt99+e42Xy93rAAAA8HnwwQfl9XqVnJyswsJC3XDDDQoKCtK9995b47vhJUInAAAATuFwODR+/Hjdd9992r17t44dO6a4uDg1bdr0nJZL6AQAAEA5DRo00KWXXuq35RE6AQAA4HPixAm9+uqr+vvf/67Dhw/L6/WWmb9hw4YaLZfQCQAAAJ9x48Zp/fr1GjhwoLp06SKHw+GX5RI6AQAA4PPxxx/rnXfe0W9+8xu/LpdHJgEAAMDnwgsvPOebhipC6AQAAIDPtGnT9OSTT2rfvn1+XS6n1wEAAODTuXNnnTx5Up07d1bjxo0VEFA2Lu7Zs6dGyyV0AgAAwGfs2LHKysrSY489platWnEjEQAAAPzv22+/1ccff6zLLrvMr8vlmk4AAAD4xMbG6sSJE35fLqETAAAAPk888YQeffRRffnllzpy5Ijy8/PL/KspTq8DAADAZ+jQoZKkQYMGlZluGIYcDodyc3NrtFxCJwAAAHxWrVplynI5vQ4AAOAHmzZt0vDhwxUfH6/Q0FCtWbOmzPx77rlHoaGhZf6VHlUsdfToUd1xxx2KjIxUVFSUxo0bp4KCgjJttm3bpuuuu04RERHq2LGj5s6dW24s77//vpKSkhQREaHu3bvrk08+qfJ6XHnllZX+qymOdAIAAPhBYWGhEhISNHLkSN16660Vtunbt6/mzZvnex0UFFRm/h133KHs7GytWLFCRUVFuvfee/XAAw9o8eLFkqT8/HwNGTJEvXr10pw5c7Rjxw6NGzdOISEhuu222yRJ33zzjcaOHavHH39cv/3tb7V8+XKNHDlSX3zxhTp06HDW9di0aVOl82saPAmdAAAAftCvXz/169ev0jZBQUEKDw+vcN6uXbv02Wef6fPPP1fnzp0lSTNnztSNN96oadOmqXXr1nr33Xfldrv1yiuvqEGDBoqPj1daWprmz5/vC50LFy5U3759dd9990mSpk6dqi+++EKLFi3Siy++eNb1GDBgQLlppz6r05JrOj0eT7kHhHo8njJfayfD7gHUM4Y8Hg/Xdlis5HeUbd1aBnW3HPsXu9TWHFBZjjGMkt/d/Pz8MvknKCio3BHKqvryyy8VGxur5s2bq2fPnnr00UcVFhYmSdq8ebNCQkJ8gVOSevfuLafTqa1bt6p///7avHmzunfvrgYNGvja9O3bV3PnztWPP/6o5s2b69tvv9Uf//jHMv326dOn3On+Mzn9E4eKi4v173//W9OnT9ejjz5ao/WWqhk609PTfT+A02VkZNR4EHbzenlDsJLXa2jXrl3qYHjlsnsw9YTX8GrXrl1s6xYr3dapu3XYv9ijdB9Tm1WUYxwOh6KiopSQkFDmuspJkyZp8uTJ1e6jb9++6t+/v6KiorR3715NmzZNw4YN0yeffCKXy6Xs7Gy1bNmyzPcEBAQoNDRU2dnZkqScnBxFRkaWaVP6PdnZ2WrevLlycnLKLadly5bKycmp0jhDQkLKTbv66qvVoEED31HTmqhW6IyNja3wSGdGRobat28vl6t2/oo7v1lr9xDqFafTobi4ODkdHIuwitPhLKk527qlfNs6dbcM+xd7lO5jaqPKcoxhGHK73dq2bVu5I501MWTIEN//d+zYUR07dlTnzp315ZdfqlevXjVbAQu1bNnynA4yVit0ulyuM37+psvlqrWhU/LPZ4qiqhy1eFupvUpqzrZuLQd1txz7F7vU9rpXlGNKz+4GBwf77fPHTxUdHa0WLVpo9+7d6tWrl8LDw3Xo0KEybYqLi3X06FHfdaCtWrUq16b09dnatGrVqkrj2rZtW5nXhmEoOztbL730khISEqq+gqfhRiIAAAAb/N///Z+OHDniC4uJiYnKy8tTamqqrrjiCknSxo0b5fV61aVLF1+bZ555RkVFRQoMDJQkrV+/3nedqCQlJSVpw4YN+sMf/uDra/369UpMTKzSuK666io5HI5yl1T++te/1iuvvFLj9SV0AgAA+EFBQUGZm3AyMzOVlpam5s2bKzQ0VDNnztTAgQMVHh6uPXv26IknnlBMTIz69u0rSYqLi1Pfvn11//33a86cOSoqKtLEiRM1ePBgtW7dWlLJpwU9//zzGjdunO6//37t3LlTCxcu1LPPPuvr96677lL//v31yiuv6Nprr9WKFSuUmpqql156qUrrkZqaWua10+nUBRdcoIYNG55TfQidAAAAfpCamlrmcUNTp06VJI0YMUKzZ8/Wjh07tGzZMuXl5SkiIkJ9+vTRlClTylwjumjRIk2YMEHJyclyOBwaOHCgZsyY4ZsfEhKi9957TxMmTNDVV1+tFi1aaMKECb7HJUlS165dtWjRIj377LOaNm2aYmJi9NZbb1XpGZ2Syt2o5C+ETgAAAD/o0aOHjh49esb577333lmXERoa6nsQ/JkkJCToo48+qrRNcnKykpOTz9pfqYULF1a57V133VXltqcidAIAANRz8+fPr1I7h8NB6AQAAEDNfPfdd6b3wYPMAAAAUCHDMM74wUDVRegEAABAGcuWLVP37t3VunVrtW7dWldeeaWWLVt2Tsvk9DoAAAB85s2bp+nTp2vs2LHq2rWrJOkf//iHHn74YR05ckT33HNPjZZL6AQAAIDPq6++qtmzZ2v48OG+addff73i4+M1Y8aMGodOTq8DAADAJzs7W0lJSeWmJyUlKTs7u8bLJXQCAADAp127dlq5cmW56StXrlRMTEyNl8vpdQAAAGjHjh3q0KGDpkyZojFjxujrr7/2XdP5zTffaMOGDXr99ddrvHyOdAIAAEA9evTQNddco9zcXP3tb39TWFiY1qxZozVr1igsLEyfffaZ+vfvX+Plc6QTAAAAWr16td5++209/vjj8nq9GjBggJ599lldeeWVflk+RzoBAACg7t2765VXXtHOnTs1c+ZM7du3TwMHDtSvf/1rvfTSS+d0E5FE6AQAAMApmjRpoltuuUVr1qzR5s2bNWjQIC1evFiXXXaZRowYUePlEjoBAABQoZiYGD300EMaP368mjZtqk8++aTGy+KaTgAAAJSzadMm/eUvf9GqVavkcDj0+9//XiNHjqzx8gidAAAAkCRlZWXp7bff1tKlS7V7924lJSVpxowZSk5OVpMmTc5p2YROAAAAaOjQodqwYYNatGihm266SSNHjlRsbKzflk/oBAAAgAIDA/Xmm2/qt7/9rVwul9+XT+gEAACAli5dauryuXsdAADADzZt2qThw4crPj5eoaGhWrNmTZn5hmFo+vTpuvTSS9W6dWslJyfrv//9b5k2R48e1R133KHIyEhFRUVp3LhxKigoKNNm27Ztuu666xQREaGOHTtq7ty55cby/vvvKykpSREREerevfs53XXuL4ROAAAAPygsLFRCQoJeeOGFCufPnTtXCxcu1Jw5c/Tpp5+qcePGGjJkiE6cOOFrc8cdd+j777/XihUrtGzZMn311Vd64IEHfPPz8/M1ZMgQtW3bVuvXr9fTTz+tmTNn6o033vC1+eabbzR27FiNHDlSGzZs0A033KCRI0dqx44dZq16lRA6AQAA/KBfv3569NFHK/x8csMw9Oc//1njx4/X9ddfr4SEBC1YsEAHDx70HRHdtWuXPvvsM7388sv69a9/rW7dumnmzJlasWKFsrKyJEnvvvuu3G63XnnlFcXHx2vIkCG68847NX/+fF9fCxcuVN++fXXfffcpLi5OU6dOVadOnbRo0SJrCnEG1bqm0+PxyOFwlJt26tfaybB7APWMIY/Hw188Fiv5HWVbt5ZB3S3H/sUutTUHVJZjDKPkdzc/P79M/gkKClJQUFC1+snMzFR2drZ69+7tmxYSEqIuXbpo8+bNGjJkiDZv3qyQkBB17tzZ16Z3795yOp3aunWr+vfvr82bN6t79+5q0KCBr03fvn01d+5c/fjjj2revLm+/fZb/fGPfyzTf58+fcqd7rdatUJnenq67wdwuoyMDL8MyA5eL28IVvJ6De3atUsdDK/8f28cKuI1vNq1axfbusVKt3Xqbh32L/Yo3cfUZhXlGIfDoaioKCUkJJS5rnLSpEmaPHlytZZf+rnlLVu2LDO9VatWysnJ8bU5fX5AQIBCQ0N935+Tk6PIyMgybUq/Jzs7W82bN1dOTk655bRs2dLXj12qFTpjY2MrPNKZkZGh9u3bm3J7vRWc36y1ewj1itPpUFxcnJwOjkVYxelwltScbd1Svm2duluG/Ys9SvcxtVFlOcYwDLndbm3btq3ckU5UX7VCp8vlKhc6T51XW0OnVPE6wSyOWryt1F4lNWdbt5aDuluO/YtdanvdK8oxpWd3g4ODz5h/qio8PFySdOjQIUVERPim5+Tk6LLLLvO1OXToUJnvKy4u1tGjR33f36pVq3JtSl+frU2rVq3OaR3OFX8KAgAAmCwqKkrh4eHasGGDb1p+fr62bt2qxMRESVJiYqLy8vKUmprqa7Nx40Z5vV516dLF1+arr75SUVGRr8369esVGxur5s2bS5KSkpLK9FPaprQfuxA6AQAA/KCgoEBpaWlKS0uTVHLzUFpamvbv3y+Hw6G7775bs2bN0ocffqjt27frD3/4gyIiInTDDTdIkuLi4tS3b1/df//92rp1q/7xj39o4sSJGjx4sFq3bi2p5KMqGzRooHHjxmnnzp1asWKFFi5cqHvuucc3jrvuukufffaZXnnlFf3nP//RjBkzlJqaqjvuuMP6opyCTyQCAADwg9TUVA0YMMD3eurUqZKkESNGaP78+br//vtVWFioBx98UHl5efrNb36j5cuXq2HDhr7vWbRokSZMmKDk5GQ5HA4NHDhQM2bM8M0PCQnRe++9pwkTJujqq69WixYtNGHCBN12222+Nl27dtWiRYv07LPPatq0aYqJidFbb72lDh06mF+EShA6AQAA/KBHjx46evToGec7HA5NmTJFU6ZMOWOb0NBQLV68uNJ+EhIS9NFHH1XaJjk5WcnJyZW2sRqn1wEAAGA6QicAAABMR+gEAACA6QidAAAAMB2hEwAAAKYjdAIAAMB0hE4AAACYjtAJAAAA0xE6AQAAYDpCJwAAAExH6AQAAIDpCJ0AAAAwHaETAAAApiN0AgAAwHSETgAAAJiO0AkAAADTEToBAABguoCqNDIMo8zX0+c5HA4ZhlHh/NqgkTNQDZ2Bdg+j/nB6S7aXhg2loCC7R1MvGA0byjAMtnWr/bytU3cLsX+xRek+pjaqLMecmn8cDocdw6tTHEYVthKv16uffvrJivEAAACcV5o1ayank5PD56rKodP3Dacl/fz8fCUkJGjbtm0KDg72/whRIepuPWpuD+puD+puPWpuj8rqfmpEInSeuyqdXq+s0A6HQwUFBXI4HBx6thB1tx41twd1twd1tx41t0dldefn4F/EdgAAAJiO0AkAAADTnXPoDAoK0qRJkxTEXYKWou7Wo+b2oO72oO7Wo+b2oO7WqdKNRAAAAMC54PQ6AAAATEfoBAAAgOkInQAAADAdoRMAAACmI3QCAADAdIROAMA583g8dg+hXjn146mB2oLQWccVFRXZPYR6p6CgQDk5OcrLy+ON2EK5ubn617/+pbS0NB05csTu4dQLu3bt0pw5cyRJLpeL7d0i//rXvzRy5EidPHnS7qEA1VKlz15H7ZSRkaHXX39dI0eOVHx8vN3DqRd27typCRMmKDc3V263W6NHj9aoUaPUvHlzu4dWp23fvl133XWXiouLVVBQoL59++rJJ59UaGio3UOrs06cOKEhQ4bowIEDOnTokJ577jlf8HS5XHYPr85KS0vTgAEDNGLECB5mjlqHI5111J49e3TDDTdoyZIlWrx4sXbt2mX3kOq8Xbt2aeDAgbrsssv09NNP69prr9WSJUu0c+dOu4dWp33//fcaOHCg+vbtq7ffflv33nuv1q9fz9FOkzVo0EBt2rTRLbfcom+//VYTJ06UVHLEk1O/5khLS9N1112n22+/XS+88IIkqbi4WG63W3zOC2oDPpGoDiosLNTDDz8st9utyy67TCtXrlTnzp31hz/8QXFxcXYPr07Ky8vT2LFj1bZtW9/pRkn63e9+p3bt2mnBggU2jq7uOnr0qG6++WZ16tRJM2bM8E3//e9/r7vuukuhoaFq3bq1IiMjbRxl3fXAAw8oNjZWLpdLr7/+uq655ho9++yz2rhxo6644goFBwfbPcQ6Izc3V506ddLVV1+tlJQUFRUV6ZFHHtGePXuUlZWlvn37atSoUYqNjbV7qMAZcXq9DmrYsKGuuuoqORwODR8+XBdccIEWLVqkBQsWEDxNkp2drWbNmun3v/+9JMntdqtBgwbq2bOn9uzZY/Po6q4TJ05o2LBh6tq1q2/arFmztGHDBh08eFABAQE6evSo3n77bV1++eU2jrRuKT2FHhQUpOPHj+uhhx6SJP3lL39Rp06dZBiGvv76a3m9XjmdnFDzB4/HoxtuuEHr1q3Tpk2b9NJLL6mwsFBXX321cnJytHXrVv3rX//SvHnzFBUVZfdwgQqxN6iDnE6nhgwZoptuukmSNHLkSP3P//yPUlNTtWDBAt+p9qKiIh08eNDOodYZl1xyiYYOHaqePXtKkgICSv6eu+CCC3TixIkybX/66SfLx1dXtW7dWoMGDVLHjh0lSUuXLtXMmTP1xhtvaM2aNXr11Vd16aWXav78+Tp58iSnIP2kNEj26dNHO3fulNPp1N13362AgADl5OSoS5cuatKkiZxOJ6fa/aRVq1Z67rnndO2112rAgAEyDEMpKSkaP368nn/+ed17773Kzc3V119/bfdQgTMidNYxpXePNmjQQA6Hw/d61KhRGjNmjC94btu2TY8//riGDRvGHZDnqPQJAddff70kyTAM35ty6Z3spWHnxRdf1LRp01RcXGzPYOuQ0m27RYsWvmnXX3+91q5dq4EDByosLEzx8fEKDg5WXl6egoKC5HA47BpunVAaIEvrGBQUpB07dkiS7rvvPh04cEBjxozR7t27de+990oSRzr9oHRbDwsL01NPPaUnn3xSt99+u8LCwnw/k+uvv15ut1vfffednUMFKsXp9Tpg3759+u677zRgwADfRfylO/pTX48ePVoOh0NvvPGGbr75Zh09elSrVq3iDsgaOLXmgYGBZWrucDhkGIYcDoeaNGmi4OBgORwOTZ8+3Xfqt/RIKKqnsm3d4/EoJCREXbp0kVQS/g3DUKNGjRQTEyOv1yuHw0HwrKZTa1565LK05p07d1ZkZKSGDh2qHTt2aNWqVbrwwgu1YMECrVu3TtnZ2QoPD7d5DWqn07f10ksaWrVqpdGjR6tx48aSSkK9x+NRYWGhoqKiuIwE5zXe+Wq5jIwM/e53v1OzZs107NgxDR8+vNwbw6mvR40apaVLl+qnn37S2rVrfaclUXVVqXmphg0bqkWLFpo5c6b+9Kc/6fPPP9dll11m08hrt7PV/fTH9Hi9Xs2cOVPr16/XBx98wBG3GjhbzUNCQnT48GHt27dP7733ni655BJJ0p133qmxY8fyyKoaqqjupwbPkJCQMu2dTqfmzZun//73v+revbtNowbOjrvXa7HDhw/rrrvuksPhUEhIiH744QeNHj1aN998sySVC0HFxcUaP368lixZor///e8Ezhqobs3nzZunxx57TI0bN9bq1at1xRVX2DTy2q26df/iiy+0evVqvf/++1qxYgVHf2rgbDUvKipSYGCgCgsLlZ2drXbt2kmS7yg/aqa62/pnn32mVatW6W9/+5s++OAD/qjFeY0jnbXYyZMn1bRpU912221q3bq1XnrpJb355puSpJtvvrnc0beAgAB169ZNo0ePJnDWUFVqfuqbbosWLRQVFaVly5bx1IBzUN26Hz58WC6XS2vWrKHuNXS2mgcGBqq4uFiNGzf2BU5JBM5zVN1tvfSa8Y8++kiXXnqpnUMHzoojnbVcVlaWWrduLUnasWOH5s6dq71792rUqFG65ZZbJJUc4eQaQv+pSs1LjwJJ4ro2P6lK3UsfVSWVPK+29Lo31Az7F3tUd1s/duyYmjRpYtt4gariIqdarnTH5PF41KFDBz344IOKjo7WkiVL9Pbbb0uSHn74Yb3++ut2DrNOqUrNx48fr0WLFkkSgdNPqlL3CRMm6H//938licDpB+xf7FHdbZ3AidqCI5110Pfff68XX3xR+/fvl8Ph0Ndff61169bpV7/6ld1Dq7OouT2ou/WouT2oO+oCQmctdbaL9dPS0nTTTTepsLBQq1evVkJCgoWjq5uouT2ou/WouT2oO+o6Tq/XEqUPB87NzZVU+cX6brfb91ikDz/8kB1TDVFze1B361Fze1B31DeEzvNcRkaGVq9eLZfLpffff1933323Dh06VOn3/PTTT9q8ebM++OADdejQwaKR1h3U3B7U3XrU3B7UHfWWgfOWx+MxnnvuOSM0NNR49NFHjdDQUGPp0qWVfo/X6zUMwzBOnjxpxRDrHGpuD+puPWpuD+qO+oxrOmuBYcOG6fPPP9ftt9+uF154ocJPvjmdwQOazwk1twd1tx41twd1R33E6fXzVOnfAkVFRQoLC1P37t312muv6b333vM9HPj0vxdOfc2OqfqouT2ou/WouT2oO+o9i46oohpKT6X885//ND799FPjp59+MgzDMJ544gkjLCzMePfdd8u0y8zMtGegdQg1twd1tx41twd1BwyDI53nGePn0ycffPCBhg0bpq1bt+rAgQOSSh7CPG7cON19991avny5HA6HZs+erYkTJ+rYsWM2j7z2oub2oO7Wo+b2oO7Az2yNvKjQ119/bURGRhqvv/66cfz48TLz8vPzjWeeecYIDQ01rr32WqN169ZGamqqTSOtO6i5Pai79ai5Pag7wI1E5xXj57+Gp0+fru3bt+svf/mLb57H45HL5fK9/vTTT7V7925de+21ateunR3DrROouT2ou/WouT2oO/CLALsHgF92SqUXiR88eND30ODSOxpLd0zfffed4uLi1K9fP+5kPAfU3B7U3XrU3B7UHSiPazptdPz4cZ08eVI//PCDTp486Zt+4YUX6ptvvlFubq7vjkap5OHAK1as0DfffCOJOxlrgprbg7pbj5rbg7oDlbDyXD5+8f333xu33nqr0a1bN+OCCy4wevbsaTz22GOGYRhGQUGBcdVVVxndunUzsrOzDa/XaxQVFRlPPfWU0bFjR2P//v02j752oub2oO7Wo+b2oO5A5bim0wbbt2/X9ddfrxtvvFGXX365QkNDtXTpUq1bt05XX3213njjDaWnp+vBBx9URkaGLr30UgUGBmrnzp1asWKFLr/8crtXodah5vag7taj5vag7sDZETotdvjwYQ0ZMkR9+vTRE088UWb6ypUr9cQTT2jAgAFauHChDMPQokWLdPjwYTVt2lQDBgzg4vIaoOb2oO7Wo+b2oO5AFdl2jLWe+u6774xu3boZ27dvN4qLiw3DKPksXsMwjB9//NF44YUXjNatWxurVq2yc5h1CjW3B3W3HjW3B3UHqoYbiSy2bds27dmzRx06dJDL5ZJhGL7P2w0JCdGwYcMUGBioPXv2lPk+gwPSNUbN7UHdrUfN7UHdgaohdFqs9DTKBx98IKn8nYpRUVGKjo5WVlZWmenc0Vhz1Nwe1N161Nwe1B2oGkKnxSIjI9WsWTMtW7ZM+/bt8033er2SpB9//FENGzZUp06d7BpinUPN7UHdrUfN7UHdgaohdFqsTZs2mj17tj777DNNnz5dO3fulCTfqZh58+bp4MGD6tatm53DrFOouT2ou/WouT2oO1A13L1uA4/HoyVLlmjixIlq166dunbtqvDwcGVmZmrdunX629/+xuMz/Iya24O6W4+a24O6A2dH6LTRli1b9PLLLys9PV0hISFKSEjQnXfeqUsuucTuodVZ1Nwe1N161Nwe1B04M0KnzTwej5xOpxwOh+/zeGEuam4P6m49am4P6g5UjN8Em5XumCTuZLQKNbcHdbceNbcHdQcqxpFOAAAAmI4jnQAAADAdoRMAAACmI3QCAADAdIROAAAAmI7QCQAAANMROgEAAGA6QicAAABMR+gEAACA6QidAAAAMB2hEwAAAKb7f/DA/yy0kRFQAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 800x575 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ===============================================================================================\n",
    "# Here we specify `up` as rgba using the matplotlib convention: rgb are floats from 0.0 and 1.0 :\n",
    "mc = mpf.make_marketcolors(base_mpf_style='yahoo',up=(0.7,1.0,0.7,0.4),down='fuchsia',\n",
    "                           edge={'up':'blue','down':'#000000'},wick='#cc6600')\n",
    "\n",
    "mco = [None,None,mc,mc,None]\n",
    "mpf.plot(df,volume=True,style='yahoo',type='candle',marketcolor_overrides=mco)\n",
    "\n",
    "# ====================================================================================\n",
    "# Here we specify `up` as rgba using the convention that rgb are ints from 0 and 255 :\n",
    "mc = mpf.make_marketcolors(base_mpf_style='yahoo',up=(178,255,178,0.4),down='fuchsia',\n",
    "                           edge={'up':'blue','down':'#000000'},wick='#cc6600')\n",
    "\n",
    "mco = [None,None,mc,mc,None]\n",
    "mpf.plot(df,volume=True,style='yahoo',type='candle',marketcolor_overrides=mco)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "#### When dealing with larger data sets we recommend setting up your marketcolor overrides as a COLUMN in your DataFrame\n",
    "#### This has two advantages:\n",
    "1. It helps ensure that your marketcolor overrides are the same length as your dataframe, and\n",
    "2. It allows you to use the dataframe's `DatetimeIndex` in order to position the override values.\n",
    "\n",
    "#### Note that you will still have to pass the `marketcolor_overrides` as a separate iterable (apart from the dataframe)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "#### Step 1: read in the data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(50, 5)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-08-29</th>\n",
       "      <td>119.559998</td>\n",
       "      <td>121.430000</td>\n",
       "      <td>118.059998</td>\n",
       "      <td>121.360001</td>\n",
       "      <td>190977200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-30</th>\n",
       "      <td>120.830002</td>\n",
       "      <td>122.430000</td>\n",
       "      <td>119.260002</td>\n",
       "      <td>121.680000</td>\n",
       "      <td>241315700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-31</th>\n",
       "      <td>122.459999</td>\n",
       "      <td>123.510002</td>\n",
       "      <td>121.300003</td>\n",
       "      <td>122.220001</td>\n",
       "      <td>301828400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close     Volume\n",
       "Date                                                                 \n",
       "2011-08-29  119.559998  121.430000  118.059998  121.360001  190977200\n",
       "2011-08-30  120.830002  122.430000  119.260002  121.680000  241315700\n",
       "2011-08-31  122.459999  123.510002  121.300003  122.220001  301828400"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('data/SPY_20110701_20120630_Bollinger.csv',index_col=0,parse_dates=True)\n",
    "df = df[['Open','High','Low','Close','Volume']].iloc[40:90] # Arbitrarily choose a 50 row subset of the data\n",
    "df.shape\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "#### Step 2: Create a new column for the overrides:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>MCOverrides</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-08-29</th>\n",
       "      <td>119.559998</td>\n",
       "      <td>121.430000</td>\n",
       "      <td>118.059998</td>\n",
       "      <td>121.360001</td>\n",
       "      <td>190977200</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-30</th>\n",
       "      <td>120.830002</td>\n",
       "      <td>122.430000</td>\n",
       "      <td>119.260002</td>\n",
       "      <td>121.680000</td>\n",
       "      <td>241315700</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-31</th>\n",
       "      <td>122.459999</td>\n",
       "      <td>123.510002</td>\n",
       "      <td>121.300003</td>\n",
       "      <td>122.220001</td>\n",
       "      <td>301828400</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close     Volume  \\\n",
       "Date                                                                    \n",
       "2011-08-29  119.559998  121.430000  118.059998  121.360001  190977200   \n",
       "2011-08-30  120.830002  122.430000  119.260002  121.680000  241315700   \n",
       "2011-08-31  122.459999  123.510002  121.300003  122.220001  301828400   \n",
       "\n",
       "           MCOverrides  \n",
       "Date                    \n",
       "2011-08-29        None  \n",
       "2011-08-30        None  \n",
       "2011-08-31        None  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['MCOverrides'] = [None]*len(df)\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "#### Step 3: Use the DatetimeIndex to position the Overrides:\n",
    "#### For demonstration purposes, let's override every Monday as black, and every Tuesday as \"blueskies\" marketcolors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Volume</th>\n",
       "      <th>MCOverrides</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2011-08-29</th>\n",
       "      <td>119.559998</td>\n",
       "      <td>121.430000</td>\n",
       "      <td>118.059998</td>\n",
       "      <td>121.360001</td>\n",
       "      <td>190977200</td>\n",
       "      <td>black</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-30</th>\n",
       "      <td>120.830002</td>\n",
       "      <td>122.430000</td>\n",
       "      <td>119.260002</td>\n",
       "      <td>121.680000</td>\n",
       "      <td>241315700</td>\n",
       "      <td>{'candle': {'up': 'w', 'down': '#0095ff'}, 'ed...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-08-31</th>\n",
       "      <td>122.459999</td>\n",
       "      <td>123.510002</td>\n",
       "      <td>121.300003</td>\n",
       "      <td>122.220001</td>\n",
       "      <td>301828400</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-01</th>\n",
       "      <td>122.290001</td>\n",
       "      <td>123.400002</td>\n",
       "      <td>120.779999</td>\n",
       "      <td>120.940002</td>\n",
       "      <td>254585900</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011-09-02</th>\n",
       "      <td>118.419998</td>\n",
       "      <td>120.870003</td>\n",
       "      <td>117.430000</td>\n",
       "      <td>117.849998</td>\n",
       "      <td>255517200</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Open        High         Low       Close     Volume  \\\n",
       "Date                                                                    \n",
       "2011-08-29  119.559998  121.430000  118.059998  121.360001  190977200   \n",
       "2011-08-30  120.830002  122.430000  119.260002  121.680000  241315700   \n",
       "2011-08-31  122.459999  123.510002  121.300003  122.220001  301828400   \n",
       "2011-09-01  122.290001  123.400002  120.779999  120.940002  254585900   \n",
       "2011-09-02  118.419998  120.870003  117.430000  117.849998  255517200   \n",
       "\n",
       "                                                  MCOverrides  \n",
       "Date                                                           \n",
       "2011-08-29                                              black  \n",
       "2011-08-30  {'candle': {'up': 'w', 'down': '#0095ff'}, 'ed...  \n",
       "2011-08-31                                               None  \n",
       "2011-09-01                                               None  \n",
       "2011-09-02                                               None  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mc = mpf.make_marketcolors(base_mpf_style='blueskies')\n",
    "for ts in df.index:\n",
    "    if 0 == ts.weekday():\n",
    "        df.loc[ts,'MCOverrides'] = 'black'\n",
    "    elif 1 == ts.weekday():\n",
    "        df.loc[ts,'MCOverrides'] = [mc]\n",
    "#mc\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== without marketcolor overrides: ===\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1120x805 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "=== WITH MARKETCOLOR OVERRIDES: ===\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1120x805 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('\\n=== without marketcolor overrides: ===')\n",
    "mpf.plot(df,volume=True,type='candle',style='yahoo',figscale=1.4)\n",
    "\n",
    "print('\\n=== WITH MARKETCOLOR OVERRIDES: ===')\n",
    "mco = df['MCOverrides'].values\n",
    "mpf.plot(df,volume=True,type='candle',style='yahoo',marketcolor_overrides=mco,figscale=1.4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TO DO:\n",
    "1. `marketcolor_overrides` should affect volume *when the override is a marketcolor object* that includes a 'volume' key.\n",
    "2. support `marketcolor_overrides` in **`mpf.make_addplot()`**\n",
    "3. support \"hollow and filled\" candles (both `mpf.plot()` and `mpf.make_addplot()`).\n",
    "4. support renko and point-and-figure.  This may be tricky (since one \"box\" may cover more and/or less than one date).  Also, is support for marketcolor overrides even needed for renko and pnf? (find out from those who commonly use them)."
   ]
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